| Land surface temperature(LST)is involved in the exchange of energy in the near-surface space,which is one of the most important parameters reflecting the surface conditions,and has been widely used in vegetation monitoring,urban climate evaluation,soil moisture estimation and other studies.With the rapid development of remote sensing technology,the LST retrieved from thermal infrared remote sensing images has the advantages of large-scale spatial coverage,multi-temporal phase and low research cost,thus becoming an important data source for multi-scale urban thermal environmental research.It is very important to accurately measure the characteristics of urban thermal environments,explore the spatiotemporal variation of LST and reveal its potential driving factors to alleviate urban heat island effect and improve urban ecological benefits.This study focuses on the application of quantitative remote sensing technology to the urban thermal environmental research,which specifically investigated the spatiotemporal variation,multi-scale driving laws,and spatial downscaling methods of urban LST.The main contribution of this paper can be summarized as the following three aspects.(1)At the phenomenological level,the spatiotemporal variation of city,city centers,blocks,and industrial parks were analyzed and summarized based on the satellite-derived LST retrieved from Landsat 8 thermal infrared remote sensing images,and the driving effects of multi-hierarchy factors to the LST was detected.(2)At the technical level,the quantitative methods of industrial heat island effect is proposed for the first time.Based on the two basic laws of geography,a general strategy coupling spatial autocorrelation and heterogeneity was developed for LST downscaling,and a non-parametric kernel-driven LST downscaling method was proposed.(3)At the theoretical and application level,this paper proposed the basic hypothesis for verifying the existence of intra-heat island effect(e.g.,the industrial heat island effect as an example)within cities.The necessity of predicting spatial autocorrelation features in the process of high-resolution LST reconstruction was discussed.The main contents and findings of this paper are as follows.(1)Spatial quantitative analysis of the potential driving factors of LST in different“centers”of polycentric citiesBased on the concept of polycentrism and the definition of polycentric city,this study focused on the spatiotemporal variation of summer surface urban heat island(SUHI)intensity in three types of city centers,namely,the main city core,the new city core and the industrial(/production)park.From 2013 to 2018,the heat island areas were distributed homogeneously in the major city core,the ratio of heat island areas decreases approximately 10%.The UHI pattern on the east-west axis was unbalanced in the Binhai new district core due to the unsaturated urban space and dynamic planning policies.In industrial park,production areas were segregated by green belts with clear boundaries.The geographical detector model(GDM)was applied to examine the extent to which eleven driving factors under four hierarchies of greenness,imperviousness(grey level),wetness and socio-economics explained the spatial heterogeneity of LST.The results showed that the response of LST to natural and social factors varied significantly among the three different city centers.For the whole city and the major city core,the imperviousness factor had the highest explanatory rate(45.9%and 27.7%,respectively)for LST spatial heterogeneity,followed by the greenness factor.In the new city core and the industrial park,the natural factors(greenness and wetness factors)played a dominant driving role in affecting LST spatial patterns.(2)Spatiotemporal patterns of LST and multi-hierarchy driving effects over urban morphological blocksFor the first time,the urban morphological blocks(UMBs)were introduced as the basic units to characterize the spatiotemporal variation of LST under various two-dimensional and three-dimensional architectural morphological conditions,at a relatively finer spatial scale.The maximum proportions of hot spots(i.e.,high-level area)all derived from low-rise and high-density blocks(LRBs)in four seasons account for 40.12%(spring),36.09%(summer),35.49(autumn),and 28.20%(winter),respectively.High-rise UMBs generate fewer hot spots,and the high-rise and high-density block(HRB)witnessed the lowest value of hot-spot Distribution Index(0.06)in winter.Generally,the blocks with lowest building height and highest building density,showed the highest LSTs compared to other types of UMBs in each season.Multifactorial LST drivers,comprising architectural morphology,land cover,land use,and functions,were rigorously examined by the GDM.The results revealed that the functional property factors were comparatively weak driving forces.Bilinear-enhanced and nonlinear-enhanced interactions were identified from pairs of factors in affecting LST over different seasons,confirming that the spatial heterogeneity of block-scale LST was the result of the combination of environmental and anthropogenic factors.(3)Quantitative method and driving analysis of the industrial heat island effectsThis study separated the industrial park from other artificial surfaces,and systematically constructed the paradigm for quantifying the industrial heat island(IHI)effect based on"LST profile-heat island intensity calculation-thermal anomaly classification-thermal landscape pattern index"using the original urban LST spatial distribution.New evidence for this intra-heat island effect was provided both on spatial and temporal scales,and the remote sensing definition of IHI effect was clarified.The results of the time series analysis of Wuhan Industrial Park from 2013 to 2020 showed that the IHI effect was aggravate in spring and summer,decreased in autumn,and was weakest in winter.The seasonal LST profiles showed a IHI footprint range of 3.35–3.4km,and the maximum LST difference with the background area can be up to 5.17℃.The fragmentation of thermal landscape patches of the industrial parks showed opposite seasonal variation characteristics.Anthropogenic heat emissions exacerbated the IHI intensity,and energy consumption in industrial production activities had a significant impact on local thermal environment.(4)A general strategy coupling spatial autocorrelation and heterogeneity for LST downscalingA general kernel-driven downscaling strategy coupling spatial autocorrelation and heterogeneity(SCSAH)was developed based on the two fundamental laws of geography and used to improve original kernel-driven methods.Under the framework of SCSAH,a novel non-parametric kernel-driven LST downscaling method(N-DLST)was also proposed,in which Bayesian non-parametric general regression(BNGR)was applied to automatically select the best regression kernel(or kernels’combination),which overcomes the shortcomings of linear models in constructing functional transformation relationships between LSTs and the kernel.In multiple models’experiments in which Landsat 8 LST data were downscaled from 300 m to 30 m,the N-DLST method outperformed the original kernel-driven methods,with the highest coefficient of determination(R~2=0.929)and lowest root mean square error(RMSE=0.853).Moreover,the SCSAH improved the accuracy of the disaggregation of radiometric surface temperature(Dis Trad)and the geographically weighted regression-based method(GWR)with an increase in R~2 by approximately 0.09 and a decrease in RMSE by more than 0.4°C,which revealed that the SCSAH can effectively enhance the LST downscaling performance of traditional kernel-driven methods over urban areas and water bodies.This paper is a quantitative remote sensing study of urban LST,focusing on its“spatiotemporal variation”,“driving factors”,and“downscaling methods”.The study summarized the spatiotemporal variation of urban thermal environment at multiple observation scales,explored their complex driving mechanisms,and proposed general and high-precision LST spatial downscaling methods.The results are of great significance for understanding the spatiotemporal patterns of urban thermal environment and provide technical support for the multi-scale urban heat island effect investigations. |
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